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To identify methods to support the node-making process in network meta-analyses (NMAs) of nonpharmacological treatments. We proceeded in two stages. First, we conducted a literature review of guidelines and methodological articles about NMAs to identify methods proposed to lump interventions into nodes. Second, we conducted a systematic review of NMAs of nonpharmacological treatments to extract methods used by authors to support their node-making process. MEDLINE and Google Scholar were searched to identify articles assessing NMA guidelines or methodology intended for NMA authors. MEDLINE, CENTRAL, and EMBASE were searched to identify reports of NMAs including at least one nonpharmacological treatment. Both searches involved articles available from database inception to March 2016. From the methodological review, we identified and extracted methods proposed to lump interventions into nodes. From the systematic review, the reporting of the network was assessed as long as the method described supported the node-making process. Among the 116 articles retrieved in the literature review, 12 (10%) discussed the concept of lumping or splitting interventions in NMAs. No consensual method was identified during the methodological review, and expert consensus was the only method proposed to support the node-making process. Among 5187 references for the systematic review, we included 110 reports of NMAs published between 2007 and 2016. The nodes were described in the introduction section of 88 reports (80%), which suggested that the node content might have been a priori decided before the systematic review. Nine reports (8.1%) described a specific process or justification to build nodes for the network. Two methods were identified: (1) fit a previously published classification and (2) expert consensus. Despite the importance of NMA in the delivery of evidence when several interventions are available for a single indication, recommendations on the reporting of the node-making process in NMAs are lacking, and reporting of the node-making process in NMAs seems insufficient.

Multivariate meta-analysis is gaining prominence in evidence synthesis research because it enables simultaneous synthesis of multiple correlated outcome data, and random-effects models have generally been used for addressing between-studies heterogeneities. However, coverage probabilities of confidence regions or intervals for standard inference methods for randomeffects models (eg, restricted maximum likelihood estimation) cannot retain their nominal confidence levels in general, especially when the number of synthesized studies is small because their validities depend on large sample approximations. In this article, we provide permutation-based inference methods that enable exact joint inferences for average outcome measures without large sample approximations. We also provide accurate marginal inference methods under general settings of multivariate meta-analyses. We propose effective approaches for permutation inferences using optimal weighting based on the efficient score statistic. The effectiveness of the proposed methods is illustrated via applications to bivariate meta-analyses of diagnostic accuracy studies for airway eosinophilia in asthma and a network meta-analysis for antihypertensive drugs on incident diabetes, as well as through simulation experiments. In numerical evaluations performed via simulations, our methods generally provided accurate confidence regions or intervals under a broad range of settings, whereas the current standard inference methods exhibited serious undercoverage properties.

We aimed to develop and evaluate an algorithm for automatically screening citations when updating living network meta-analysis (NMA). Our algorithm learns from the initial screening of citations conducted when creating an NMA to automatically identify eligible citations (i.e., needing full-text consideration) when updating the NMA. We evaluated our algorithm on four NMAs from different medical domains. For each NMA we constructed sets of initially screened citations and citations to screen during an update that took place 2 years after the conduct of the NMA. We encoded free text of citations (title and abstract) using word embeddings. On top of this vectorized representation, we fitted a logistic regression model to the set of initially screened citations to predict the eligibility of citations screened during an update. Our algorithm achieved 100% sensitivity on two NMAs (100% [95% confidence interval 93–100] and 100% [40–100] sensitivity), and 94% (81–99) and 97% (86–100) on the remaining two others. For all NMAs, our algorithm would have spared to manually screen 1,345 of 2,530 citations, decreasing the workload by 53% (51–55), while missing 3 of 124 eligible citations (2% [1–7]), none of which were finally included in the NMAs after full-text consideration. For updating an NMA after 2 years, our algorithm considerably diminished the workload required for screening, and the number of missed eligible citations remained low.

Background Sarcopenia is a serious public health concern among older adults worldwide. Exercise is the most common intervention for sarcopenia. This study aimed to compare the effectiveness of different exercise types for older adults with sarcopenia. Methods Randomized controlled trials (RCTs) that examined the effectiveness of exercise interventions on patient‐important outcomes for older adults with sarcopenia were eligible. We systematically searched MEDLINE, Embase and Cochrane Central Register of Controlled Trials via Ovid until 3 June 2022. We used frequentist random‐effects network meta‐analyses to summarize the evidence and applied the Grading of Recommendations, Assessment, Development, and Evaluations framework to rate the certainty of evidence. Results Our search identified 5988 citations, of which 42 RCTs proved eligible with 3728 participants with sarcopenia (median age: 72.9 years, female: 73.3%) with a median follow‐up of 12 weeks. We are interested in patient‐important outcomes that include mortality, quality of life, muscle strength and physical function measures. High or moderate certainty evidence suggested that resistance exercise with or without nutrition and the combination of resistance exercise with aerobic and balance training were the most effective interventions for improving quality of life compared to usual care (standardized mean difference from 0.68 to 1.11). Moderate certainty evidence showed that resistance and balance exercise plus nutrition (mean difference [MD]: 4.19 kg) was the most effective for improving handgrip strength (minimally important difference [MID]: 5 kg). Resistance and balance exercise with or without nutrition (MD: 0.16 m/s, moderate) were the most effective for improving physical function measured by usual gait speed (MID: 0.1 m/s). Moderate certainty evidence showed that resistance and balance exercise (MD: 1.85 s) was intermediately effective for improving physical function measured by timed up and go test (MID: 2.1 s). High certainty evidence showed that resistance and aerobic, or resistance and balance, or resistance and aerobic exercise plus nutrition (MD from 1.72 to 2.28 s) were intermediately effective for improving physical function measured by the five‐repetition chair stand test (MID: 2.3 s). Conclusions In older adults with sarcopenia, high or moderate certainty evidence showed that resistance exercise with or without nutrition and the combination of resistance exercise with aerobic and balance training were the most effective interventions for improving quality of life. Adding nutritional interventions to exercise had a larger effect on handgrip strength than exercise alone while showing a similar effect on other physical function measures.

This article describes conceptual advances of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group guidance to evaluate the certainty of evidence (confidence in evidence, quality of evidence) from network meta-analysis (NMA). Application of the original GRADE guidance, published in 2014, in a number of NMAs has resulted in advances that strengthen its conceptual basis and make the process more efficient. This guidance will be useful for systematic review authors who aim to assess the certainty of all pairwise comparisons from an NMA and who are familiar with the basic concepts of NMA and the traditional GRADE approach for pairwise meta-analysis. Two principles of the original GRADE NMA guidance are that we need to rate the certainty of the evidence for each pairwise comparison within a network separately and that in doing so we need to consider both the direct and indirect evidence. We present, discuss, and illustrate four conceptual advances: (1) consideration of imprecision is not necessary when rating the direct and indirect estimates to inform the rating of NMA estimates, (2) there is no need to rate the indirect evidence when the certainty of the direct evidence is high and the contribution of the direct evidence to the network estimate is at least as great as that of the indirect evidence, (3) we should not trust a statistical test of global incoherence of the network to assess incoherence at the pairwise comparison level, and (4) in the presence of incoherence between direct and indirect evidence, the certainty of the evidence of each estimate can help decide which estimate to believe. •The application of the Grading of Recommendations Assessments, Development, and Evaluation approach to a number of network meta-analyses in the 3 years since the original guidance publication has led to advances that have strengthened the conceptual basis.•We present, discuss, and illustrate four conceptual advances. These are based on two principles: we need to rate the certainty of the evidence of each pairwise comparison within a network separately and that we need to consider both the direct and indirect evidence contributing to each network estimate.•Although maximizing the efficiency of the process is desirable, as illustrated in the conceptual advances, use of these strategies requires careful judgment.

The aim of the study was to develop a Grading of Recommendations, Assessment, Development and Evaluation (GRADE) summary of findings (SoF) table format that displays the critical information from a network meta-analysis (NMA). We applied a user experience model for data analysis based on four rounds of semistructured interviews. We interviewed 32 stakeholders who conduct or use MA. Four rounds of interviews produced six candidate NMA-SoF tables. Users found a final NMA-SoF table that included the following components highly acceptable: (1) details of the clinical question (PICO), (2) a plot depicting network geometry, (3) relative and absolute effect estimates, (4) certainty of evidence, (5) ranking of treatments, and (6) interpretation of findings. Using stakeholder feedback, we developed a new GRADE NMA-SoF table that includes the relevant components that facilitate understanding NMA findings and health decision-making.

Network meta-analysis is a technique for comparing multiple treatments simultaneously in a single analysis by combining direct and indirect evidence within a network of randomized controlled trials. Network meta-analysis may assist assessing the comparative effectiveness of different treatments regularly used in clinical practice and, therefore, has become attractive among clinicians. However, if proper caution is not taken in conducting and interpreting network meta-analysis, inferences might be biased. The aim of this paper is to illustrate the process of network meta-analysis with the aid of a working example on first-line medical treatment for primary open-angle glaucoma. We discuss the key assumption of network meta-analysis, as well as the unique considerations for developing appropriate research questions, conducting the literature search, abstracting data, performing qualitative and quantitative synthesis, presenting results, drawing conclusions, and reporting the findings in a network meta-analysis.

Network meta-analysis (NMA) is becoming a popular statistical tool for analyzing a network of evidence comparing more than two interventions. A particular advantage of NMA over pairwise meta-analysis is its ability to simultaneously compare multiple interventions including comparisons not previously trialed together, permitting intervention hierarchies to be created. Our aim was to develop a novel graphical display to aid interpretation of NMA to clinicians and decision-makers that incorporates ranking of interventions. Current literature was searched, scrutinized, and provided direction for developing the novel graphical display. Ranking results were often found to be misinterpreted when presented alone and, to aid interpretation and effective communication to inform optimal decision-making, need to be displayed alongside other important aspects of the analysis including the evidence networks and relative intervention effect estimates. Two new ranking visualizations were developed—the ‘Litmus Rank-O-Gram’ and the ‘Radial SUCRA’ plot—and embedded within a novel multipanel graphical display programmed within the MetaInsight application, with user feedback gained. This display was designed to improve the reporting, and facilitate a holistic understanding, of NMA results. We believe uptake of the display would lead to better understanding of complex results and improve future decision-making. •The ability to conduct a network meta-analysis (NMA) and thus rank included interventions is a powerful tool; however, the presentation of rankings in particular can easily be misinterpreted.•Two new ranking visualizations have been developed—the ‘Litmus Rank-O-Gram’ and the ‘Radial SUCRA’ curve plot—to improve the presentation of ranking results.•The new visualizations have been embedded within a novel multipanel display, alongside presentation of evidence networks and relative effect estimates, programmed within MetaInsight, an interactive web-based application for NMA, with user feedback gained.•The multipanel display provides an all-encompassing graphic to encourage a holistic understanding of the NMA, in turn, improving the interpretation and dissemination of NMAs for clinicians and decision-makers.

Background Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines. Results To better facilitate the conduct and reporting of NMAs, we have created an R package called “BUGSnet” ( B ayesian inference U sing G ibbs S ampling to conduct a Net work meta-analysis). This R package relies upon Just Another Gibbs Sampler (JAGS) to conduct Bayesian NMA using a generalized linear model. BUGSnet contains a suite of functions that can be used to describe the evidence network, estimate a model and assess the model fit and convergence, assess the presence of heterogeneity and inconsistency, and output the results in a variety of formats including league tables and surface under the cumulative rank curve (SUCRA) plots. We provide a demonstration of the functions contained within BUGSnet by recreating a Bayesian NMA found in the second technical support document composed by the National Institute for Health and Care Excellence Decision Support Unit (NICE-DSU). We have also mapped these functions to checklist items within current reporting and best practice guidelines. Conclusion BUGSnet is a new R package that can be used to conduct a Bayesian NMA and produce all of the necessary output needed to satisfy current scientific and regulatory standards. We hope that this software will help to improve the conduct and reporting of NMAs.

Background Sphingosine‐1‐phosphate receptor modulators (S1PRM) are used to treat relapsing multiple sclerosis (MS). Each drug has a different S1PR‐subtype selectivity. They target the G‐protein coupled S1P receptors and exert significant immunomodulatory effects, such as preventing the formation of new CNS lesions and the reactivation of pre‐existing lesions. Objective This study aims to explore the efficacy and safety of S1PRM in treating MS. Methods A systematic literature search of PubMed, Embase, and Cochrane databases was conducted in August 2024. Randomized Controlled Trials that evaluated the efficacy of S1PRM in patients with MS were included. Changes in Annualized Relapse Rate and incidence of adverse effects were chosen as primary outcomes. Standardized mean differences (SMD) and odds ratio (OR) were calculated. Confidence interval was kept at 95%. Individual interventions were compared using the Surface Under Cumulative Ranking Curve (SUCRA). The risk of bias was assessed by the Cochrane risk‐of‐bias tool for randomized trials (RoB 2). Results The search query resulted in a total of 1750 studies. After screening, 17 studies were included in the final analysis, with a population of 16,006. Fingolimod (1.25 mg) was significantly associated with a decreased ARR (SMD = −0.4422, 95% CI = [−0.5450 to −0.3394], p‐value < 0.0001, SUCRA = 92.65%). Whereas, ozanimod (1 mg) was associated with the lowest number of new Gadolinium‐enhanced lesions (SMD = −0.6516, 95% CI = [−0.8944 to −0.4087], p‐value < 0.0001, SUCRA = 86.38%). Siponimod (1.25 mg) was associated with the least number of adverse events (OR = 0.4606, 95% CI = [0.1893 to 1.1205], p = 0.0874, SUCRA = 93.20%). Almost all of the studies had a low risk of bias. Conclusion Fingolimod (1.25 mg) and ozanimod (1 mg) had the best efficacy, and siponimod (1.25 mg and 0.25 mg) had the best safety profile among the S1PRM. Further longitudinal studies should be conducted to assess the long‐term effects of these drugs on patient‐reported outcomes.